In medicine, CAD systems are meant to ultimately output potential anomalies within medical images. Prior art methods and systems have typically gathered 2D and 3D approaches with a preferred and most successful process being a coarse to fine approach, detecting multiple “coarse” initial patches further refined by a classifier that only the “best” candidates may survive.
A first method for polyp identification in the colon is disclosed in International PCT application No. WO 98/37517 entitled “Automatic analysis in virtual endoscopy”. The proposed methods and systems require the segmentation of an organ of interest, typically a colon. Upon successful segmentation, a mesh, i.e. a set of isosurfaces oriented from normals, is used to interactively visualize the colon, in addition to support a “shape characteristics analysis” comprising the step of determining a convexity value for each population representing an amount and direction of curvature.
Such method and the equivalent subsequent ones that were proposed, such as the one disclosed in U.S. Pat. No. 7,369,638, base their strategy on the identification of characteristic shapes of polyps when compared to the smooth appearance of the colonic mucosa for example. Thus, an accurate representation of the organ based on a segmentation process is required in order to accurately identify shapes of interest. The skilled addressee will appreciate that lesions of random shapes may not be detected. For the case of CT colonography, also called virtual colonoscopy, the skilled addressee will appreciate that most prior art methods are meant to identify polypoid anomalies (of spherical shapes), not cancers neither masses (of random shapes with potentially no spherical protuberance).
However, and as mentioned in U.S. Pat. No. 7,236,620 (hereinafter '620) entitled “Computer-aided detection methods in volumetric imagery”, detectors based on curvature calculation use derivative processes which are susceptible to produce spurious outputs due to noise in the input imagery. Such limitation also affect every equivalent method involving gradient and iso-surfaces considering that the zero value iso-surface of the distance map yields the object surface and the derivative of the distance map yields the surface normal, i.e. a mesh, as mentioned in 1998, Using distance maps for accurate surface representation in sampled volumes, Gibson Sarah F. F., Mitsubishi Electric Research Laboratory, IEEE. The skilled addressee will appreciate that any such distance map requires object segmentation, as stated in U.S. Pat. No. 7,113,617 entitled “Method of computing sub-pixel Euclidean distance maps”. As mentioned, a method of generating a distance map includes the step of identifying a boundary curve of a source image. The skilled addressee will appreciate that, for the case of CAD methods in CT Colonography for example, prior art segmentation and distance map determinations rely on the accurate identification of the inner wall of the colonic mucosa on which surface normals are determined (such as mesh, gradient).
To overcome the above-mentioned limitations of “derivative processes”, '620 patent discloses a method based on simple spherical summations. The method requires a binary image, i.e. segmented, to be input, from which a shape is defined based on the ratio of segmented elements falling within the ratio of two spherical summation processes, involving one 2D image at a time but overlooking a 3D region. Such methodology does reduce the amount of processing time required and is less susceptible to noise, but is still really dependent on the image segmentation processing. As such, it only decreases the processing time and complexity required, but does not improve the detection output as it shifts the difficulty toward the segmentation stage.
Concurrently, Gokturk introduced a three-dimensional pattern recognition method to detect shapes in medical images at the Biomedical computation Stanford 2000 symposium proceedings entitled “recognizing polyps from 3D CT colon data” where a random slicing through a candidate volume is used in order to extract shape features from 2D slices, the latter being input in a support vector machine (SVM) classifier further in charge to identify polyp candidates. This was further detailed in “A statistical 3D pattern processing method for computer-aided detection of polyps in CT colonography, Gokturk and al., IEEE transaction on medical imaging, vol. 20(12) December 2001” and led to U.S. Pat. No. 7,346,209. These developments lead to an approach similar to that of '620 patent involving the use of 2D gradient summations in order to reduce noise artifacts, expecting that summation and smoothing operations would help enhance the difference between homogeneous and inhomogeneous structures, where local image gradient at pixels other than edges would be more significant than it would be for homogeneous structures. As such, a limitation arises in the definition of the edges, which is the necessity to have an accurate segmentation process of the structure of interest. Additionally, Gokturk methods were more about constructing shape signatures to be further input in classifiers than polyp detection in itself.
Following both preceding concepts and combining them, Cathier disclosed a method and system for using cutting planes for colon polyp detection in U.S. Pat. No. 7,447,342 (hereinafter '342). The method and system disclosed involve the reslicing of volumes throughout a dataset in order to detect small and round shaped traces on any of these planes. However, and as the previous techniques discussed herein, this method requires that the image be preprocessed to distinguish the colon from other structures in the image with high accuracy necessary to successful polyp detection. Furthermore, the skilled addressee will appreciate that such technique is meant to be used for polypoid shape recognition but does not address the needs of cancers and masses detection (featuring random shapes).
To overcome the limitation of '342 method with respect to its sensitivity to a binarization threshold, US Patent Application published under No. 2009/0016583 discloses the use of a Divergence Gradient Field Response (DGFR). As mentioned, such method allows for the detection of circles directly in the gradient domain, instead of edges or magnitude of the gradient as in the case of the '342 patent. However, two intrinsic limitations are expressed in such methodology. First, a Divergence Gradient Field Response identifies circles of given sizes and, as the size of the polyp to be found is not a-priori known, one needs to compute DGFR for a multitude of sub-volumes (sub-sampled volumes) covering the complete range of polyps sizes. Thus, a choice such as at which point to stop sub-sampling has to be made, thereby limiting the size of the smallest and largest polyp to be found. This is an issue of template matching techniques well known to the skilled addressee. The second limitation is that DGFR detects circles, although polyps might depict shapes more complex than simple circles. Unfortunately, this technique does not address the needs of cancers and masses detection (i.e. random shapes).
Furthermore, in addition to an always existing segmentation limitation, it may be observed that these methodologies have two distinct steps: candidate detection and false-positive reduction, whether through density analysis or shape analysis for example. As well, the above mentioned methods are meant to detect circular/ellipsoidal shapes to further detect polyps. It is to be understood that looking for a sphere in a digital dataset will be equivalent to either detect round shapes or detect local/global curvatures. There is thus a lack of methods suitable to detect lesions of various sizes and shapes, as expressed by Dr C. Robinson at 2009 European Congress of Radiology (ECR) in Vienna: “CAD algorithms were developed to detect polyps in the context of screening”, which study was meant to investigate the performance of a commercial CADe device based on “reader adjustable sphericity-settings” for cancer candidates generation. The author said that “The CAD algorithm was applied to each dataset at four sphericity settings (0, 50, 75, 100). Seventy-five was the default manufacturer's setting, 100 (highest sphericity) detected a more curvy shape, and a single observer characterized all of the CAD marks”. Respectively at sphericity settings of 0; 50; 75 and 100, the results in terms of “Sensitivity; False-Positive rate” were {90.2%; 65}, {88.6%; 57}, {87.1%; 45} and {74.2%; 24}. Such a high false-positive rate demonstrates the inability of such morphology-based algorithm to accurately identify cancers and other lesions of varying shapes, considering high sensitivity may only be achieved if anything else (not of clinical interest) is picked. This is well expressed by Dr. C. Robinson: “The detection of cancer increases with decreasing sphericity, at the expense of decreasing specificity.”
Finally, amongst other limitations is the fact that some methods involve simple threshold to distinguish the colon from other structure to differentiate the lumen from tissues. Although there is no such “simple threshold” method, a clear limitation of such methods would be the inability to handle CT colonography datasets resulting from reduced preparation with fluid/stool tagging where more than “simply separating air/tissue” is required. Indeed, and considering such reduced preparation, the skilled addressee will understand that tagged-residual might represent small/round characteristics similar to polyps.
It would therefore be desirable to provide an improved method and apparatus that will overcome at least one of the above-identified drawbacks.